Abstract
In the article an approach to solving the problem of inductive synthesis of the models of biological systems according to clinical trials is suggested. Suggested approach to inductive synthesis of biological models on the base of results of clinical trials allows essentially decrease computational complexity of this problem. Formalization of biological models in the form of graph of parameters allows use well developed mathematical apparatus of theory of graphs, which suggest effective methods of models transformation. Nowadays suggested approach is used in Almazov Cardiological Center for automatic medical data processing.
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References
Lushnov, A., Lushnov, M.: Medical Information Systems: Multidimensional Analyses of Medical and Ecology Data. Helicon Plus, SpB (2013)
Kupershtoh, V., Mirkin, B., Trofimov, V.: The sum of internal links as indicator of classification results quality. Avtomatika i Telemekhanika 3, 133–141 (1976). (in Russian)
Kriete, A., Eils, R.: Computational Systems Biology. Elsevier Academic Press, Cambridge (2006)
Akbari, Z., Unland, R.: Automated determination of the input parameter of DBSCAN based on outlier detection. In: Iliadis, L., Maglogiannis, I. (eds.) AIAI 2016. IFIP AICT, vol. 475, pp. 280–291. Springer, Cham (2016). doi:10.1007/978-3-319-44944-9_24
Stankova, E.N., Balakshiy, A.V., Petrov, D.A., Shorov, A.V., Korkhov, V.V.: Using technologies of OLAP and machine learning for validation of the numerical models of convective clouds. In: Gervasi, O., et al. (eds.) ICCSA 2016, Part III. LNCS, vol. 9788, pp. 463–472. Springer, Cham (2016). doi:10.1007/978-3-319-42111-7_36
Raba, N.O., Stankova, E.N.: On the problem of numerical modeling of dangerous convective phenomena: possibilities of real-time forecast with the help of multi-core processors. In: Murgante, B., Gervasi, O., Iglesias, A., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2011. LNCS, vol. 6786, pp. 633–642. Springer, Heidelberg (2011). doi:10.1007/978-3-642-21934-4_51
Freedman, D.: Statistical Models: Theory and Practice, 2nd edn. Cambridge University Press, Cambridge (2009)
Gill, P.R., Murray, W., Wright, M.H.: The Levenberg-Marquardt method. In: Practical Optimization, pp. 136–137. Academic Press, London (1981)
Zaki, M.J., Meira Jr., W.: Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press, Cambridge (2014)
Feldman, R., Sanger, J.: The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press, Cambridge (2006)
Leskovec, J., Rajaraman, A., Ullman, J.: Mining Massive Datasets. Stanford University, Stanford (2014)
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This work was partially financially supported by Government of Russian Federation, Grant 074-U01
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Osipov, V., Lushnov, M., Stankova, E., Vodyaho, A., Zukova, N. (2017). Inductive Synthesis of the Models of Biological Systems According to Clinical Trials. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10404. Springer, Cham. https://doi.org/10.1007/978-3-319-62392-4_8
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DOI: https://doi.org/10.1007/978-3-319-62392-4_8
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